{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Xorbits Inference (Xinference)\n",
    "\n",
    "[Xinference](https://github.com/xorbitsai/inference) is a powerful and versatile library designed to serve LLMs, \n",
    "speech recognition models, and multimodal models, even on your laptop. It supports a variety of models compatible with GGML, such as chatglm, baichuan, whisper, vicuna, orca, and many others. This notebook demonstrates how to use Xinference with LangChain."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Installation\n",
    "\n",
    "Install `Xinference` through PyPI:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install \"xinference[all]\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Deploy Xinference Locally or in a Distributed Cluster.\n",
    "\n",
    "For local deployment, run `xinference`. \n",
    "\n",
    "To deploy Xinference in a cluster, first start an Xinference supervisor using the `xinference-supervisor`. You can also use the option -p to specify the port and -H to specify the host. The default port is 9997.\n",
    "\n",
    "Then, start the Xinference workers using `xinference-worker` on each server you want to run them on. \n",
    "\n",
    "You can consult the README file from [Xinference](https://github.com/xorbitsai/inference) for more information.\n",
    "## Wrapper\n",
    "\n",
    "To use Xinference with LangChain, you need to first launch a model. You can use command line interface (CLI) to do so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model uid: 7167b2b0-2a04-11ee-83f0-d29396a3f064\n"
     ]
    }
   ],
   "source": [
    "!xinference launch -n vicuna-v1.3 -f ggmlv3 -q q4_0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A model UID is returned for you to use. Now you can use Xinference with LangChain:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "' You can visit the Eiffel Tower, Notre-Dame Cathedral, the Louvre Museum, and many other historical sites in Paris, the capital of France.'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from langchain.llms import Xinference\n",
    "\n",
    "llm = Xinference(\n",
    "    server_url=\"http://0.0.0.0:9997\", model_uid=\"7167b2b0-2a04-11ee-83f0-d29396a3f064\"\n",
    ")\n",
    "\n",
    "llm(\n",
    "    prompt=\"Q: where can we visit in the capital of France? A:\",\n",
    "    generate_config={\"max_tokens\": 1024, \"stream\": True},\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Integrate with a LLMChain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "A: You can visit many places in Paris, such as the Eiffel Tower, the Louvre Museum, Notre-Dame Cathedral, the Champs-Elysées, Montmartre, Sacré-Cœur, and the Palace of Versailles.\n"
     ]
    }
   ],
   "source": [
    "from langchain.chains import LLMChain\n",
    "from langchain.prompts import PromptTemplate\n",
    "\n",
    "template = \"Where can we visit in the capital of {country}?\"\n",
    "\n",
    "prompt = PromptTemplate(template=template, input_variables=[\"country\"])\n",
    "\n",
    "llm_chain = LLMChain(prompt=prompt, llm=llm)\n",
    "\n",
    "generated = llm_chain.run(country=\"France\")\n",
    "print(generated)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lastly, terminate the model when you do not need to use it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "!xinference terminate --model-uid \"7167b2b0-2a04-11ee-83f0-d29396a3f064\""
   ]
  }
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